Association of Sodium, Potassium and Sodium-to-Potassium Ratio with Urine Albumin Excretion among the General Chinese Population

Mixed evidence was published regarding the association of sodium, potassium and sodium-to-potassium ratio (Na/K ratio) with renal function impairment. This study was conducted to further explore the relationship between sodium, potassium, NA/K ratio and kidney function in the general adult Chinese population. We performed a cross-sectional analysis using the baseline data from the Action on Salt China (ASC) study. 5185 eligible general adult participants from the baseline investigation of the ASC study were included in this analysis. Sodium, potassium and albumin excretion were examined from 24-h urine collection. Albuminuria was defined as albumin excretion rate (AER) greater than or equal to 30 mg/24-h. Mixed linear regression models, adjusted for confounders, were fitted to analyze the association between sodium, potassium and Na/K ratio, and natural log transformed AER. Mixed effects logistic regression models were performed to analyze the odds ratio of albuminuria at each quintile of sodium, potassium and Na/K ratio. The mean age of the participants was 49.5 ± 12.8 years, and 48.2% were male. The proportion of albuminuria was 7.5%.The adjusted mixed linear models indicated that sodium and Na/K ratio was positively associated with natural log transformed AER (Sodium: β = 0.069, 95%CI [0.050, 0.087], p < 0.001; Na/K ratio: β = 0.026, 95%CI [0.012, 0.040], p < 0.001). Mixed effects logistic regression models showed that the odds of albuminuria significantly increased with the quintiles of sodium (p < 0.001) and Na/K ratio (p = 0.001). No significant association was found between potassium and the outcome indicators. Higher sodium intake and higher Na/K ratio are associated with early renal function impairment, while potassium intake was not associated with kidney function measured by albumin excretion.

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Physician Perceptions on Quality of Life and Resuscitation Preferences for Extremely Early Newborns

American Journal of Perinatology ◽

10.1055/s-0041-1733782 ◽

2021 ◽

Author(s):

Danielle LoRe ◽

Christopher Mattson ◽

Dalia M. Feltman ◽

Jessica T. Fry ◽

Kathleen G. Brennan ◽

...

Keyword(s):

Quality Of Life ◽

Logistic Regression ◽

Regression Models ◽

Mixed Effects ◽

Cross Sectional Survey ◽

Cross Sectional ◽

Comfort Care ◽

Logistic Regression Models ◽

Extremely Preterm

Objective The study aimed to explore physician views on whether extremely early newborns will have an acceptable quality of life (QOL), and if these views are associated with physician resuscitation preferences. Study Design We performed a cross-sectional survey of neonatologists and maternal fetal medicine (MFM) attendings, fellows, and residents at four U.S. medical centers exploring physician views on future QOL of extremely early newborns and physician resuscitation preferences. Mixed-effects logistic regression models examined association of perceived QOL and resuscitation preferences when adjusting for specialty, level of training, gender, and experience with ex-premature infants. Results A total of 254 of 544 (47%) physicians were responded. A minority of physicians had interacted with surviving extremely early newborns when they were ≥3 years old (23% of physicians in pediatrics/neonatology and 6% in obstetrics/MFM). The majority of physicians did not believe an extremely early newborn would have an acceptable QOL at the earliest gestational ages (11% at 22 and 23% at 23 weeks). The majority of physicians (73%) believed that having an extremely preterm infant would have negative effects on the family's QOL. Mixed-effects logistic regression models (odds ratio [OR], 95% confidence interval [CI]) revealed that physicians who believed infants would have an acceptable QOL were less likely to offer comfort care only at 22 (OR: 0.19, 95% CI: 0.05–0.65, p < 0.01) and 23 weeks (OR: 0.24, 95% CI: 0.07–0.78, p < 0.02). They were also more likely to offer active treatment only at 24 weeks (OR: 9.66, 95% CI: 2.56–38.87, p < 0.01) and 25 weeks (OR: 19.51, 95% CI: 3.33–126.72, p < 0.01). Conclusion Physician views of extremely early newborns' future QOL correlated with self-reported resuscitation preferences. Residents and obstetric physicians reported more pessimistic views on QOL. Key Points

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Serum GlycA level is a candidate biomarker for disease activity in systemic lupus erythematosus and for proliferative status of lupus nephritis, independent of renal function impairment.

10.1101/493809 ◽

2018 ◽

Author(s):

Tim Dierckx ◽

Sylvie Goletti ◽

Laurent Chiche ◽

Laurent Daniel ◽

Bernard Lauwerys ◽

...

Keyword(s):

Systemic Lupus Erythematosus ◽

Logistic Regression ◽

Renal Function ◽

Lupus Nephritis ◽

Serum Albumin ◽

Disease Activity ◽

Lupus Erythematosus ◽

Regression Models ◽

Systemic Lupus ◽

Logistic Regression Models

Objective: Glycoprotein acetylation (GlycA) is a novel biomarker for chronic inflammation, associated to cardiovascular risk. Serum GlycA levels are increased in several inflammatory diseases, including systemic lupus erythematosus (SLE). We investigated the relevance of serum GlycA measurement in SLE and lupus nephritis (LN). Methods: GlycA was measured by NMR in 194 serum samples from patients and controls. Comparisons were performed between groups. Clinical and biological parameters were tested for correlation with GlycA levels. The predictive value of GlycA to differentiate proliferative from non-proliferative LN was determined using logistic regression models. Results: GlycA was correlated to C-reactive protein (CRP), neutrophil count, proteinuria and the SLE disease activity index (SLEDAI), and inversely with serum albumin. GlycA was higher in active (n=105) than in quiescent (n=39) SLE patients, in healthy controls (n=29), and in patients with non-lupus nephritis (n=21), despite a more altered renal function in the latter. In patients with biopsy-proven active LN, GlycA was higher in proliferative (n=32) than non-proliferative (n=11) LN, independent of renal function and proteinuria level. Logistic regression models showed that, in univariate models, GlycA outperforms traditional biomarkers. A bivariate model using GlycA and BMI better predicted the proliferative status of LN than a model comprising CRP, renal function (eGFR), serum albumin, proteinuria, C3 consumption and the presence of anti-dsDNA antibodies. Conclusion: Serum GlycA is elevated in SLE, and correlates with disease activity and LN. Serum GlycA, which summarizes different inflammatory processes, could be a valuable biomarker to discriminate proliferative from non-proliferative LN and should be tested in large, prospective cohorts.

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Infection Dynamics of Mycoplasma bovis and Other Respiratory Mycoplasmas in Newly Imported Bulls on Italian Fattening Farms

Pathogens ◽

10.3390/pathogens9070537 ◽

2020 ◽

Vol 9 (7) ◽

pp. 537 ◽

Cited By ~ 1

Author(s):

Salvatore Catania ◽

Michele Gastaldelli ◽

Eliana Schiavon ◽

Andrea Matucci ◽

Annalucia Tondo ◽

...

Keyword(s):

Logistic Regression ◽

Gel Electrophoresis ◽

Regression Models ◽

Denaturing Gradient Gel Electrophoresis ◽

Mixed Effects ◽

Mycoplasma Bovis ◽

Logistic Regression Models ◽

Infection Dynamics ◽

16S Rdna Pcr ◽

Gradient Gel Electrophoresis

Italian beef production is mainly based on a feedlot system where calves are housed with mixed aged cattle often in conditions favourable to bovine respiratory disease (BRD). In Veneto, an indoor system is also used for imported bulls around 300–350 kg. Mycoplasmas, in particular Mycoplasma bovis and Mycoplasma dispar, contribute to BRD in young calves, but their role in the disease in older cattle has not been investigated. In this study, ten heads of cattle were selected from each of the 24 groups kept in 13 different farms. Bulls were sampled by nasal swabbing at 0, 15, and 60 days after arrival for Mycoplasma isolation. Identification was carried out by 16S-rDNA PCR followed by denaturing gradient gel electrophoresis. M. bovis, M. dispar, and M. bovirhinis were identified, and prevalence was analysed by mixed-effects logistic regression models. This showed that most bulls arrived free of M. bovis, but within two weeks, approximately 40% became infected, decreasing to 13% by the last sampling. In contrast, the prevalence of M. dispar was not dependent on time or seasonality, while M. bovirhinis only showed a seasonality-dependent trend. The Italian fattening system creates an ideal environment for infection with M. bovis, probably originating from previously stabled animals.

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Extending the interpretation and utility of mixed effects logistic regression models

Preventive Veterinary Medicine ◽

10.1016/0167-5877(95)92833-j ◽

1995 ◽

Vol 24 (3) ◽

pp. 187-201 ◽

Cited By ~ 12

Author(s):

Edward R. Atwill ◽

Hussni O. Mohammed ◽

Janet M. Scarlett ◽

Charles E. McCulloch

Keyword(s):

Logistic Regression ◽

Regression Models ◽

Mixed Effects ◽

Logistic Regression Models

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Improving the odds? How to pick the winner of the English Derby

Comparative Exercise Physiology ◽

10.3920/cep10017 ◽

2014 ◽

Vol 10 (1) ◽

pp. 57-62

Author(s):

D.J. Marlin ◽

J.M. Williams ◽

T. Parkin

Keyword(s):

Logistic Regression ◽

Confidence Interval ◽

Regression Models ◽

Mixed Effects ◽

Point Estimate ◽

Prize Money ◽

Logistic Regression Models ◽

Predictive Variables ◽

Time Period ◽

Predictive Probability

Many consider the English Derby on Epsom Downs to be ‘The Blue Riband of the Turf’. The Epsom Derby has been run annually since 1780 and the colt Diomed was the first winner. Today the Epsom Derby, run over 1.5 miles, is one of five classic races and is the second leg of the English Triple Crown, preceded by the 2,000 Guineas and followed by the St Leger. The prize money for 2010 has been in excess of £1.25 million. To the best of our knowledge, whilst epidemiological techniques have previously been applied in an attempt to identify risk factors for injury, the purpose of the present study, which we believe is unique, was to use an epidemiological approach to analyse factors that may be predictive of success (or failure) in a single race over the course of a number of consecutive years: The Epsom Derby. Information on the horses competing in the last 22 runnings of the Epsom Derby between 1988 and 2009. Univariate and multivariable single-level and mixed effects logistic regression models were developed using winning the Epsom Derby as the dependent variable. Between 1988 and 2009 in 22 runnings of the Derby, a total of 344 horses started the Epsom Derby. The number of runners in the race has varied between 12 and 25 over the same time period. On average the probability of winning the Derby between 1988 and 2009 was approximately 6% (22/344), without accounting for any potentially predictive variables. Variables that were related to an increased chance of success were being the favourite (odds ratio (OR) 4.75; 95 % confidence interval (CI) 1.58-14.3; P=0.006), the number of 2-year old wins (OR 1.45; CI 1.03-2.04; P=0.03), being foaled in Ireland (OR 2.80; CI 1.12-7.04; P=0.041) and having the same jockey in all races throughout the horses career up to and including the Derby (OR 2.53; CI 1.0-6.41; P=0.05). The highest predictive probability was for horses that started the race as a favourite, were Irish bred, had been ridden by a single jockey and had won twice as a 2-year old. Although the point estimate for this probability was 52% the degree of uncertainty around this estimate was wide, i.e. the 95% CI was 17.5 to 86.5%. Nevertheless even at the lower confidence interval this still represents a significant improvement on the approximately 6% chance of picking a winner at random. In conclusion, using mixed effects logistic regression models would allow one to improve the odds of picking the winner of the Epsom Derby over the past 22 runnings.

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Mixed Effects Logistic Regression Models for Multiple Longitudinal Binary Functional Limitation Responses with Informative Drop-Out and Confounding by Baseline Outcomes

Biometrics ◽

10.1111/j.0006-341x.2002.00137.x ◽

2002 ◽

Vol 58 (1) ◽

pp. 137-144 ◽

Cited By ~ 17

Author(s):

Thomas R. Ten Have ◽

Beth A. Reboussin ◽

Michael E. Miller ◽

Allen Kunselman

Keyword(s):

Logistic Regression ◽

Regression Models ◽

Functional Limitation ◽

Mixed Effects ◽

Drop Out ◽

Logistic Regression Models

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Convergence in Mixed Effects Logistic Regression Models

10.22186/jyi.36.2.18-35 ◽

2019 ◽

Author(s):

Alexandrea Churchill ◽

Grace Kissling

Keyword(s):

Logistic Regression ◽

Regression Models ◽

Mixed Effects ◽

Logistic Regression Models

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Abstract P159: In-Hospital Lifestyle Interventions to Prevent Secondary Stroke Among At-Risk Patients Varies by Patient Characteristics and Over Time: Analyses From a Large, Multi-Hospital Network

Circulation ◽

10.1161/circ.135.suppl_1.p159 ◽

2017 ◽

Vol 135 (suppl_1) ◽

Author(s):

Elizabeth Baraban ◽

Lindsay Lucas ◽

Kateri Spinelli

Keyword(s):

At Risk ◽

Logistic Regression ◽

Weight Management ◽

Regression Models ◽

Risk Groups ◽

Mixed Effects ◽

Logistic Regression Models ◽

Risk Patients ◽

To Receive ◽

Over Time

Introduction/Objective: Recent studies show that targeted interventions on lifestyle factors such as weight management and diet can be successful in reducing ischemic stroke (ISC) and transient ischemic attack (TIA) rates. The objective of this study was to examine which subpopulations of patients at risk for secondary stroke presenting to a hospital with an ISC or TIA were more likely to receive interventions in a multi-hospital health system. Methods: Data from 26 hospitals participating in a multi-state healthcare system stroke registry, from January 2009 to December 2015, were used. Patients admitted with a diagnosis of ISC or TIA discharged to home were included. Patients on comfort care or those not discharged home were excluded. At-risk groups included patients with Body Mass Index (BMI) ≥ 25 and those prescribed medication for hypertension or high cholesterol in-hospital. Risk-related interventions included educational material given to patients during admission. Mixed effects logistic regression models with backward elimination were used to identify significant predictors of receiving the intervention from the following variables: year of discharge, age, insurance (private, Medicare, other/self-pay), BMI grouping, ambulation status, length of stay, stroke severity, and medical histories of family stroke, previous stroke or TIA, atrial fibrillation, coronary artery disease, heart failure, dyslipidemia, hypertension, and drug/alcohol abuse. Results: A total of 19,661 patients met the inclusion criteria. Of the 8,334 patients with a BMI ≥ 25, 57% (n=4,717) received weight management intervention. Of the 9,676 prescribed medication for hypertension, 55% (n=5,348) received information on antihypertensive diet. Of the 10,999 patients prescribed medication to lower cholesterol, 64% (n=7,088) received cholesterol lowering diet information. From 2009 to 2015, interventions increased for patients with a BMI ≥ 25 (40% to 66%), prescribed medication for hypertension (37% to 53%), and prescribed medication to lower cholesterol (39% to 67%). The mixed effects logistic regression models showed that all risk groups were significantly less likely to receive intervention if they had lower BMIs, were unable to ambulate versus able to ambulate alone, and had no family history of stroke. For those on medication for cholesterol, patients with Medicare were significantly less likely to receive the intervention compared to those on private insurance or other payment types (AOR=0.78, p<0.001). Conclusions: This large patient cohort demonstrates there are improvement opportunities for in-hospital secondary stroke prevention efforts. Over time, prevention efforts have increased for at-risk patients, but many are still not receiving it. The disparity in intervention rates suggest that a more targeted strategy to educate at-risk populations may need to be developed.

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